# Exploiting Consistency Theory for Modeling Twitter Hashtag Adoption

## TL;DR

This paper introduces a matrix factorization method incorporating consistency theory to predict Twitter hashtag adoption, improving trend prediction accuracy by modeling user behavior and hashtag propagation.

## Contribution

It presents a novel low-rank weighted matrix factorization approach that integrates social consistency theory as a regularization for modeling hashtag adoption.

## Key findings

- Outperforms baseline models in predicting hashtag usage
- Effectively captures user behavior and trend propagation
- Enhances real-time trend identification capabilities

## Abstract

Twitter, a microblogging service, has evolved into a powerful communication platform with millions of active users who generate immense volume of microposts on a daily basis. To facilitate effective categorization and easy search, users adopt hashtags, keywords or phrases preceded by hash (#) character. Successful prediction of the spread and propagation of information in the form of trending topics or hashtags in Twitter, could help real time identification of new trends and thus improve marketing efforts. Social theories such as consistency theory suggest that people prefer harmony or consistency in their thoughts. In Twitter, for example, users are more likely to adopt the same trending hashtag multiple times before it eventually dies. In this paper, we propose a low-rank weighted matrix factorization approach to model trending hashtag adoption in Twitter based on consistency theory. In particular, we first cast the problem of modeling trending hashtag adoption into an optimization problem, then integrate consistency theory into it as a regularization term and finally leverage widely used matrix factorization to solve the optimization. Empirical experiments demonstrate that our method outperforms other baselines in predicting whether a specific trending hashtag will be used by users in future.